# Comparison of Machine Learning Methods for Assigning Software Issues to Team Members

arXiv: https://arxiv.org/abs/2307.00009 · HTML: https://mlsanity.com/paper/2307.00009/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 62 references that are in the map.
- First posted on arXiv 18 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging](https://mlsanity.com/paper/1801.01275/) (2018)
- [TaDaa: real time Ticket Assignment Deep learning Auto Advisor for customer support, help desk, and issue ticketing systems](https://mlsanity.com/paper/2207.11187/) (2022)
- [Automatic Bug Triage using Semi-Supervised Text Classification](https://mlsanity.com/paper/1704.04769/) (2017)
- [On the feasibility of automated prediction of bug and non-bug issues](https://mlsanity.com/paper/2003.05357/) (2020)
- [Issue Auto-Assignment in Software Projects with Machine Learning Techniques](https://mlsanity.com/paper/2104.01717/) (2021)
- [Reduction of Redundant Rules in Association Rule Mining-Based Bug Assignment](https://mlsanity.com/paper/1807.08906/) (2018)

## Abstract (arXiv)

Software issues contain units of work to fix, improve, or create new threads during the development and facilitate communication among the team members. Assigning an issue to the most relevant team member and determining a category of an issue is a tedious and challenging task. Wrong classifications cause delays and rework in the project and trouble among the team members. This paper proposes a set of carefully curated linguistic features for shallow machine learning methods and compares the performance of shallow and ensemble methods with deep language models. Unlike the state-of-the-art, we assign issues to four roles (designer, developer, tester, and leader) rather than to specific individuals or teams to contribute to the generality of our solution. We also consider the level of experience of the developers to reflect the industrial practices in our solution formulation. We collect and annotate five industrial data sets from one of the top three global television producers to evaluate our proposal and compare it with deep language models. Our data sets contain 5324 issues in total. We show that an ensemble classifier of shallow techniques achieves 0.92 for issue assignment in accuracy which is statistically comparable to the state-of-the-art deep language models. The contributions include the public sharing of five annotated industrial issue data sets, the development of a clear and comprehensive feature set, the introduction of a novel label set, and the validation of the efficacy of an ensemble classifier of shallow machine learning techniques.